Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking (2402.09369v1)

Abstract: Pretrained LLMs have revolutionized many applications but still face challenges related to cultural bias and a lack of cultural commonsense knowledge crucial for guiding cross-culture communication and interactions. Recognizing the shortcomings of existing methods in capturing the diverse and rich cultures across the world, this paper introduces a novel approach for massively multicultural knowledge acquisition. Specifically, our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages. Leveraging this valuable source of data collection, we construct the CultureAtlas dataset, which covers a wide range of sub-country level geographical regions and ethnolinguistic groups, with data cleaning and preprocessing to ensure textual assertion sentence self-containment, as well as fine-grained cultural profile information extraction. Our dataset not only facilitates the evaluation of LLM performance in culturally diverse contexts but also serves as a foundational tool for the development of culturally sensitive and aware LLMs. Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI, to promote a more inclusive and balanced representation of global cultures in the digital domain.

Massively Multi-Cultural Knowledge Acquisition and Benchmarking in LLMs: Insights from the CultureAtlas Dataset

Introduction to Multi-Cultural Knowledge in LMs

The expansion of pretrained LLMs into diverse applications underscores an emerging challenge: cultural bias and misinterpretation. The crux of this issue lies in the models’ inherent design, which may not adequately capture the world’s cultural diversity. This shortfall not only hinders the application of LMs in global contexts but also perpetuates a Western-centric digital narrative. Addressing this challenge is crucial for fostering inclusive and fair AI systems that accurately reflect global cultural diversities.

The CultureAtlas Benchmark

A novel contribution towards rectifying cultural biases in LMs is the construction of the CultureAtlas dataset. This dataset distinguishes itself by its scope, encompassing over 1000 sub-country regions and 2000+ ethnolinguistic groups. The data collection process leverages a network of culturally relevant Wikipedia documents, expanded through links to associated pages, ensuring a broad capture of cultural nuances. This methodical approach facilitates the generation of high-quality data samples that are substantiated by human assessment, showcasing a 90+% accuracy rate. By covering an extensive range of geo-cultural regions and ethnolinguistic identities, CultureAtlas presents a significantly more diverse benchmark than prior works in the domain.

Data Acquisition and Processing

CultureAtlas's data acquisition initiates from Wikipedia, known for its reliable content due to public audits. Targeting an initial set of documents related to cultural topics, the process exploits linked pages to broaden its coverage. This expansive data collection spans various cultural dimensions such as country, sub-country regions, ethnicity, religion, age, gender, marital status, and occupation. This multi-faceted approach not only yields a comprehensive set of positive cultural knowledge samples but also curates negative samples to assess the models' robustness in identifying non-factual cultural information.

Benchmark Construction and Evaluation

The benchmark construction process emphasizes a balanced representation of cultural diversity. It meticulously categorizes data based on geographical regions and ethnolinguistic groups, surpassing previous works in terms of coverage and depth. The evaluation of state-of-the-art foundation models on this benchmark revealed interesting insights, such as

• The performance of different LMs varies significantly across cultural contexts, with newer models like Vicuna showcasing better understanding than their predecessors.
• A notable performance variance was observed across cultural topics, suggesting that LMs have a dissimilar grasp on diverse cultural domains.
• Importantly, the paper highlighted the challenge LMs face in incorporating fine-grained cultural nuances into their reasoning capabilities.

Future Directions

This work paves the way for a new direction in AI research focused on massively multi-cultural knowledge acquisition. It underscores the importance of developing culturally sensitive and aware LMs that can navigate the complex landscape of global cultures with fairness and inclusivity. Future research could explore incorporating multimedia content to enhance cultural understanding or expanding coverage to include more low-resource settings and languages.

Ethical Considerations

The paper highlights the ethical implications of constructing and utilizing a dataset like CultureAtlas. Ensuring balanced cultural representation and avoiding the perpetuation of biases are paramount. The dataset's development adheres to principles that prioritize fairness and inclusivity, aiming to reflect a broad spectrum of human cultural diversity. These efforts are vital for mitigating cultural bias in LMs and enhancing their applicability in global contexts.

Conclusion

The introduction of the CultureAtlas dataset marks a significant leap towards understanding and addressing the cultural biases in LLMs. By providing a platform of greatly diversified cultural knowledge, this research not only improves the accuracy and fairness of LMs but also contributes to the broader goal of making AI systems more inclusive and representative of the global population. Future advancements in this domain hold the potential to bridge cultural gaps in digital communication, fostering a more equitable digital future.